Triple

T1245809
Position Surface form Disambiguated ID Type / Status
Subject Florence E26762 entity
Predicate locatedIn P40 FINISHED
Object Tuscany E34826 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Tuscany | Statement: [Florence, locatedIn, Tuscany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tuscany
Context triple: [Florence, locatedIn, Tuscany]
  • A. Tuscany chosen
    Tuscany is a central Italian region renowned for its rolling landscapes, historic cities like Florence and Siena, and its pivotal role in art, culture, and the birth of the Renaissance.
  • B. Umbria
    Umbria is a central Italian region known for its historic hill towns, medieval architecture, and rich cultural heritage.
  • C. Liguria
    Liguria is a coastal region in northwestern Italy known for its picturesque Riviera, including the Cinque Terre and the city of Genoa.
  • D. Senigallia
    Senigallia is a historic coastal town in Italy’s Marche region, known for its Adriatic seaside resort, Renaissance heritage, and well-preserved old town.
  • E. Emilia-Romagna
    Emilia-Romagna is a region in northern Italy known for its rich culinary traditions, historic cities, and strong industrial and agricultural economy.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a4948689d08190b3a4a3f388c02148 completed March 1, 2026, 7:33 p.m.
NER Named-entity recognition batch_69a4bf65c41c8190b4c65e015d1264c0 completed March 1, 2026, 10:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69ada0b77f008190893627daee29c441 completed March 8, 2026, 4:15 p.m.
Created at: March 1, 2026, 7:47 p.m.